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EMRI_MC: A GPU-based Python code for Bayesian inference of EMRI waveforms

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arxiv 2311.17174 v2 pith:Y4QLSXZK submitted 2023-11-28 gr-qc astro-ph.COastro-ph.HEastro-ph.IM

Ippocratis D. Saltas , Roberto Oliveri This is my paper
classification gr-qcastro-ph.COastro-ph.HEastro-ph.IM
keywords codeemriwaveformsbayesianefficientpythonaccountingalgorithm
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We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves (GW) produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so--called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. The code provides a helpful resource for forecasts for interferometry missions in the milli-Hz scale, e.g the satellite-mission LISA.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion

    gr-qc 2025-08 conditional novelty 6.0 of 10

    A hybrid flow-matching plus parallel tempering MCMC pipeline recovers EMRI source parameters from simulated Taiji data under broad priors.

  2. Gravitational wave propagation in bigravity in the late universe

    gr-qc 2025-07 unverdicted novelty 5.0 of 10

    Exact solutions and uniform approximations for GW modes in bigravity in de Sitter yield regime-dependent luminosity distances, a new bound from GW170817, and retained coherence between massless and massive signal components.

  3. Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sources

    cs.IR 2026-06 unverdicted novelty 4.0 of 10

    COGNI is a production conversational BI system with indexing, routing, retrieval, and caching layers that reports 88-94% accuracy metrics on internal enterprise benchmarks for mixed structured and unstructured data.

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